class_docstr: Base mixin for all Linear modules. The interface represented by this module
  is:
  
  hidden_out = activation(hidden_in * weight + bias)
  
  The format and dtype of the weight and bias tensors are not defined and implementations
  may compress as necessary. Must support a bias.
class_docstr_tok: ["Base","mixin","for","all","Linear","modules",".","The","interface","represented","by","this","module","is",":","hidden_out","=","activation","(","hidden_in","*","weight","+","bias",")","The","format","and","dtype","of","the","weight","and","bias","tensors","are","not","defined","and","implementations","may","compress","as","necessary",".","Must","support","a","bias","."]
code: def __init__(
      self,
      config: DSLinearConfig,
      implementation_config: Dict[str, Any],
  ) -> None:
      super().__init__(config, implementation_config)
  
file_path: DeepSpeed/deepspeed/inference/v2/modules/interfaces/linear_base.py
imports: from abc import abstractmethod
  from typing import Any, Dict, Optional, Type
  import torch
  from deepspeed.runtime.config_utils import DeepSpeedConfigModel
  from ..ds_module import DSModuleBase
  from ..module_registry import DSModuleRegistryBase
  from ..configs import DSLinearConfig
  from ...inference_parameter import InferenceParameter
label_desc: Use image node_id 2 to create a new DSLinearBase object from inherited base classes: DSModuleBase with example: obj = DSLinearBase(config, implementation_config)